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arXiv · 1910.14262

W-Net BF: DNN-based Beamformer Using Joint Training Approach

Yuichiro Koyama·Bhiksha Raj
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Abstract

Acoustic beamformers have been widely used to enhance audio signals. The best current methods are DNN-powered variants of the generalized eigenvalue beamformer, and DNN-based filterestimation methods that directly compute beamforming filters. Both approaches, while effective, have blindspots in their generalizability. We propose a novel approach that combines both approaches into a single framework that attempts to exploit the best features of both. The resulting model, called a W-Net beamformer, includes two components: the first computes a noise-masked reference which the second uses to estimate beamforming filters. Results on data that include a wide variety of room and noise conditions, including static and mobile noise sources, show that the proposed beamformer outperforms other methods in all tested evaluation metrics.

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Yuichiro Koyama, Bhiksha Raj. 2020-02-29. W-Net BF: DNN-based Beamformer Using Joint Training Approach. https://arxiv.org/abs/1910.14262

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